Nipun Jain

🌟 Education Milestone! 🌟 I'm thrilled to share that I recently conducted my first Large Language Model (LLM) sentiment analysis using SQL in Snowflake! 📊✨ As part of my journey towards earning my Master of Science in Data Analytics and Insights at the University of Wisconsin-Madison, I've been diving deep into the world of data. With almost one-third of my classes completed, this hands-on project was an incredible opportunity to apply what I've learned so far. One key application learned was using the LLM sentiment function to analyze large amounts of text data. As an HR leader, organizations can gain valuable insights into employee sentiment by using employee experience feedback, particularly from Great Place to Work free-form data. Michael C. Bush, is GPTW using LLM sentiment functions to see this data and help organizations use it effectively. The detailed analysis would allow HR and organizational leaders to understand the nuances in all the comments, enhancing the overall employee experience and driving better HR strategies with insights from all of the data, not just the standard questions with scaled answers. Check out the image below for a snapshot of my work. I analyzed product reviews to extract valuable insights into customer sentiment. It is fascinating to see how natural language processing can be leveraged to drive better business decisions. The world of data analysis is changing quickly. Get on board, or be left in the dust. A big shoutout to my professors and classmates for their continuous support and inspiration. On Wisconsin! #DataAnalytics #LLM #SentimentAnalysis #Snowflake #SQL #UWMadison #LifelongLearning #DataDriven #CustomerInsights #HR #NPS #GreatPlaceToWork #DataScience #gmashrm

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Check out this Q&A with Capital One’s Head of AI Product, @Aparna Sinha, where she shares advice on building enterprise AI/ML capabilities. The conversation touches on the importance of product management in AI, Capital One’s AI/ML priorities, advice for those looking to break into the industry and so much more! Read it now on the Capital One tech blog

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Landing an analytics job interview is one thing, but acing it is another. Having conducted multiple interviews for analytics roles, where only 1 in 3 candidates gets selected, I’ve noticed a common trait among those who succeed: their ability to clearly and effectively explain their resume. It’s not just about having the right experience—it’s about how well you can articulate it. #AnalyticsCareer #InterviewTips #CareerGrowth

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Thrilled to achieve the Microsoft Certified: Fabric Analytics Engineer Associate certification! 🙌 Heartfelt thanks to my COE team, Tiger Analytics, and amazing colleagues for their unwavering support. Excited to apply my skills and drive impactful analytics solutions! 🚀 #MicrosoftCertification #FabricAnalyticsEngineer #ProfessionalGrowth #TeamSupport #AchievementUnlocked #ContinuousLearning #CareerMilestone #DataAnalytics #DataEngineering #TigerTribe #TigerAnalytics

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Many people ask me, what do you think about the data job market, why are so many people still unemployed? The truth is: Data is no longer an easy field to break into because everyone wants it and the biggest demand for talent is in the mid/senior level roles-- which you need multiple years of experience for. Entry-level roles are super competitive due to the saturation of the job market. These roles aren't in as much demand by companies, but they're in huge demand on the job market-- leaving many candidates frustrated by the intense competition. In 2024, things have changed. Not only has there been a large increase in the number of data jobs, there's been an even bigger increase in the number of people moving into data careers. And don't forget that social media has greatly influenced glorifying the field by presenting it as easy to break into, the 50 LPA+ YouTube thumbnails are not true, please don't follow such things. So how will you stand out? What should be your strategy? I recommend slowing down your job search, focusing on the basics, and taking your skills to another level. Your quick course and projects might not be enough in this market, and that's okay. Take a few more months to practice and learn the information more deeply, work on real-time projects, engage with people who have good experience and you'll become a completely different candidate in the job market. This job market is all about being strategic, so don't rush your job search. Focus on learning, building, and making connections. It's okay if it takes 3-12 months. Focus on the long game. Feel free to ping me for any doubts.

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In Introduction to Snowflake, I learned fundamental concepts about Snowflake's distinct architecture and database concepts. The session covered topics such as DDL and DML, the importance of data types and their conversions, as well as hands-on exercises focusing on Snowflake's functionality. Additionally, the course delved into joins, sub-queries, and query optimization, with a special emphasis on handling semi-structured data, particularly JSON.

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One of the most important and commonly asked Data Engineering #interview questions is : 📍Why Spark transformations are lazy? Now, there are 2 types of operations in spark: 1. Action. e.g. display, save etc. 2. Transformation. e.g. filter, map etc. 📍How do lazy transformations help? Let's say, I am trying to figure out Top 50 salaries from the employee table and I want to order them by name. 📝If I order first and then filter,then that is a waste of time and resources. Spark being lazy,will filter first and then sort.Even if I ask it do otherwise. Spark will execute the command in an optimised manner only when an action is called and will sit idle no matter how many transformations are called before that. That's how being lazy helps Spark to be more efficient. >>Link in the comments below: #Dataengineer #dataanalytics #datascience #problemsolving #dataoptimize #datacouple #video #interview

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Alteryx Realworld Problem Statements Excited to share how I leveraged Alteryx to tackle four challenging data problems! 🔍 Case Study 1: Data has been collected on monthly expenditures on the restaurants for individuals in two neighboring towns. First, combine the datasets (dataset 1 and dataset 2) and identify the top 10% of people who spend the most on restaurants. Of these people, how many are females 50 years of age or older? 🎯 Case Study 2: Open dataset contains information on the airports and the other dataset contains information on the runways at those airports. Which airport in the "US-region has the longest lighted runway? 🔗 Case Study 3: The provided dataset shows the results from a trivia night. Six friends are split into two teams to play several round of trivia. In each round, there are multiple questions worth one point each, and every player answer separately. Which player earned the highest point total? Which team earned the highest point total? 📑 Case Study 4: Refer to car entrance sheet: which month has largest amount of vehicles that enter the park 💡 Alteryx Tools Used: Union Tool: Combines multiple datasets into one by stacking them vertically. It matches columns either by name or position. Sample Tool: Selects a specified number or percentage of rows from the dataset, allowing for random or sequential sampling. Join Tool: Merges two datasets based on a common field (key). It outputs matched records and optionally unmatched records from either dataset. Text to Columns Tool: Splits a single text field into multiple columns based on a specified delimiter, useful for parsing structured text data. Summarize Tool: Performs various summary calculations such as count, sum, average, min, max, and group by operations on the dataset, providing aggregated insights. Alteryx's intuitive interface and powerful tools made solving these problems not only possible but efficient and enjoyable. If you're looking to streamline your data processes and gain deeper insights, I highly recommend exploring what Alteryx has to offer! 💼🔍 GitHub: https://lnkd.in/gKx5S9dj #DataAnalytics #Alteryx #DataScience #DataIntegration #DataPreparation #BusinessIntelligence #DataDriven #BusinessIntelligence #GitHub #Analytics #DataReporting #Reporting

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🚨 Enhancing Data Monitoring with Snowflake Alerts! 🚨 I'm excited to share how we've leveraged Snowflake's alert feature to notify our team of data deviations. I am super happy how snowflake improving the features for the business need as well !!💪✨ ❄️ #Snowflake #DataMonitoring #Alerts #DataIntegrity #BusinessIntelligence

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I had a conversation with a friend about how data engineering job requirements have become overwhelming. When I started as an ETL developer, the job required SQL, database knowledge, and an ETL tool. Now, data engineers need to know Python, NoSQL databases, distributed systems, Spark, cloud, DevOps, real-time streaming technology, Linux, some ML, GenAI, and more. Despite this, salaries remain equivalent to what they were 10 years ago. Essentially, we're required to know more for the same pay as before. Can someone explain why this is happening? :) #dataengineer

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There's one core career differentiator for analytics professionals. It's a skillset that everyone know about but very few practice. Leadership. Everyone else is focusing on technical skills and tools. Few people are focused on how the team can use those tools to build the team's capability. It's in your organization's best interest for you to lead. If you lead, you help others improve and help the team accomplish its objective. Your impact isn't limited to the work that you do individually. Your impact is scaled through others that you lead, coach, and teach. So when you think about how to differentiate yourself, Think about leading.

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🚀 Empowering Decisions with a Dynamic Sales Dashboard! 📈 Excited to share this sales Performance Dashboard i created using Tableau. It provides a comprehensive view of key business metrics. This tool has been instrumental in converting complex data into actionable insights. 💡 Key Insights from the Dashboard: Profit by Product Category: Visualizing profit distribution across key product lines like Accessories, Clothing, and Phones offers a clear picture of which categories are driving the most revenue. This helps in identifying opportunities for inventory adjustments, marketing focus, or potential product expansion. Customer Demographics Breakdown: A gender distribution pie chart provides insight into how products resonate with different customer segments, revealing a near-even split between male and female buyers., The age group analysis shows which demographics are most engaged, from 25-34 to 55-64, offering valuable data for targeted marketing efforts and customer engagement strategies. Sales Team Performance: The Total Transactions per Salesperson chart brings transparency to team contributions, making it easier to recognize top performers like Remota and Chinazam, as well as to identify areas for additional support. This insight aids in setting data-driven sales targets and optimizing strengths across the team. Geographic Profit Distribution: The state-wise profit map provides a detailed view of sales performance across different regions, highlighting areas of high profitability and regions with growth potential. Such insights are critical for region-specific marketing campaigns and optimizing logistics and inventory management. 🎯 Strategic Benefits of the Dashboard: Real-Time Decision Making: With key data points visualized in one place, the dashboard enables quicker responses to market changes and more informed decisions. Enhanced Collaboration: It serves as a common reference point for aligning sales, marketing, and operational strategies. Focus on Growth: Understanding both high and low-performing areas allows for targeted efforts, maximizing what works well and addressing underperformance, ultimately driving sustainable growth. 🛠️ How It Was Built: Developed using Tableau, the dashboard leverages powerful visualization tools, including charts, maps, and pie chart. The design focuses on balancing aesthetic appeal with practical usability, ensuring that visuals are clear and accessible, even for those less familiar with data analytics. 📈 Building this dashboard has been an enriching process, turning raw numbers into a strategic asset that drives better business outcomes. It demonstrates the power of well-presented data to align goals and fuel smarter strategies. #DataVisualization #SalesPerformance #Tableau #BusinessStrategy #DataDriven #SalesGrowth #DataInsights #TableauDashboard #BusinessIntelligence 10Alytics Matthew Oladiran Ibrahim Ibrahim Adeiza Suleman Efemena Ikpro Chukwuemeka Ikpa

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Excited to share my latest Tableau Public project! 🌟 Dive into my interactive dashboard that showcases insightful trends and data visualizations. Check it out and let me know your thoughts and suggestions! #DataVisualization #TableauPublic #AnalyticsInAction #Insights #hiring #BusinessIntelligence

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Excited to share that I've successfully earned the Databricks Certified Generative AI Engineer Associate certification! I am thrilled to be among the few who have had the opportunity to take this novel exam and achieve this milestone. This certification has equipped me with cutting-edge knowledge and skills in Generative AI, which I am eager to apply in real-world scenarios. The exam assesses one's ability to design and implement solutions using Databricks, focusing on LLM-enabled applications. It covers essential topics such as problem decomposition, model selection, and leveraging current generative AI tools to build comprehensive solutions. Key technologies include Vector Search for semantic similarity, Model Serving for efficient deployment, MLflow for lifecycle management, and Unity Catalog for data governance. In my current internship, the insights and expertise gained from this certification are proving to be incredibly valuable. From building and optimizing data pipelines to implementing advanced AI models, the knowledge I’ve acquired is directly contributing to our innovative projects. A big thank you to my manager Vinay Reddy who supported me along the way. Here's to continuing the journey of learning and innovation in the exciting field of AI! #GenerativeAI #Databricks #VectorSearch #ModelServing #MLflow #UnityCatalog #Certification #DataEngineering

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🚀 Exciting day at Snowflake Data Cloud World Tour Mumbai 2024! ❄️ I had the privilege of attending the Snowflake Data Cloud World Tour in Mumbai, and here are some key takeaways from insightful sessions on the future of data and AI: 📊 Open Table Formats & Iceberg Support Exploring integration possibilities with Apache Iceberg and Polaris Discussions on ACID compliance, performance optimization, and data evolution 🛡️ Snowflake Horizon Unveiling a vision for unified data governance and security across clouds Concepts for simplifying management of data and apps from a centralized interface 🤖 Generative AI & LLMs Introduction to the Snowflake Arctic family of Large Language Models (LLMs) Exploring Snowflake Cortex as a pathway to democratize AI Emphasis on the "AI to Data, not Data to AI" approach for enterprises 🔒 Data Governance & Security Highlighting the importance of unified security and compliance measures Showcasing potential applications of Dynamic Data Masking for data protection 💻 Snowpark Container Service & ML API Demonstrations of running AI and full-stack apps within the Snowflake ecosystem Exploring possibilities for direct ML model execution and predictions 🔗 Native Apps & Data Sharing Discussing streamlined approaches to building and sharing data applications Insights into enhancing operational collaborations through data sharing 📄 Document AI & Unstructured Data Previewing tools aimed at extracting insights from unstructured data The event showcased Snowflake's vision for innovation, open standards, and efficient data management. It was fascinating to see the proposed solutions for various data challenges, from governance to AI implementation. Kudos to the speakers, including Sanjay Deshmukh Vijayant Rai Rahul Jain 🇮🇳 Kamesh Sampath and Sumeet Tandure Excited to contemplate how these concepts might shape our future data strategies! #SnowflakeWorldTour #DataCloud #AI #DataGovernance #Innovation

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My Data Engineering Interview Experience on Airflow and GCP Two months ago, I had a data engineering interview focused on Airflow and GCP for a role requiring 3-5 years of experience. The interviewer, a data engineering expert with more than a decade of experience, was very patient and had a wonderful ability to breaking down intricate ideas into simple explanations. Here are the questions I faced: Daily Activities and Challenges - What are some challenges in day-to-day data engineering activities? - Describe the day-to-day activities of a data engineer with a GCP background. Airflow and DAGs - Create a DAG for one task in Python Airflow. - Explain the step-by-step process of DAG creation. - What are the differences between DummyOperator and PythonOperator? - Explain the DAG creation process from the start, including Airflow setup. - Which Airflow versions are compatible with Python? - Explain the DAG execution process from code to end UI. GCP -A scenario question involving GCS, BigQuery, and Dataproc. -Questions on Spark architecture and its components. Coding Questions -Basic and coding questions on Python and SQL. #DataEngineering #InterviewExperience #InterviewPreparation

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📣 📣 📣 We're hiring a Sr Data Scientist on the Online Reinforcement Learning Team at Home Depot!!!! 📣 📣 📣 https://lnkd.in/g7raJdVY I can't overstate how much I enjoy working within this team and partnering with fantastic business stakeholders, engineers, other data scientists, and product managers. Please apply or share if you or someone you know has strong experience and/or interest in growing their skills in: - Data Engineering (SQL, Python, Vertex) - Advanced Experimentation (AB testing, Multi-armed Bandits) - Statistics (causal inference and hypothesis testing) - Machine Learning (supervised and unsupervised techniques) - Deep Learning (online reinforcement learning, contextual multi-arm bandits, deep Q-learning) - Problem Solving (identifying business areas that can benefit from RL techniques and optimization) - Stakeholder Management (driving implementation and adoption among a team of business stakeholders, engineers, product managers, and data scientists) - Data Storytelling (translating complex methods and results across business leaders and IT experts) #DataScience #ReinforcementLearning #HomeDepot #TechJobs #MachineLearning #RetailTech #ai #DeepLearning #DataAnalytics #Data #BigData #DataScientist

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|| Exploring Connections Between Ancient Wisdom and Modern Science || As a curious student of both science and various religions, I recently discovered an interesting link between the old and the new. At MPD, I recently finished a course in Data Analytics. A few days ago, while i was diving into numerology and astrology through an article, I noticed that both fields use numbers to predict future events. Whether analyzing data or reading astrological charts, these methods combine fixed and changing elements to forecast outcomes—though not always accurately. This similarity made me think about the intellectual achievements of our ancestors who, without modern technology, developed such sophisticated systems to understand the universe through numbers. I'm sharing these thoughts as a personal reflection on how different forms of knowledge can intersect, highlighting the timeless wisdom that continues to influence us today. #DataAnalytics #Numerology #Astrology #AncientWisdom #ModernScience Thanks Pragya Gulati for helping me understand and validate my thoughts.

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